{"id":"W2475020744","doi":"10.1109/icuas.2016.7502546","title":"Vision-based forest fire detection in aerial images for firefighting using UAVs","year":2016,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Firefighting; Artificial intelligence; Computer science; Optical flow; Computer vision; Feature (linguistics); Fire detection; Thresholding; Pixel; Remote sensing; Image (mathematics); Geography; Engineering; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001746612,0.0003749444,0.0003499093,0.001210456,0.0002253269,0.0003178395,0.0002752261,0.000279794,0.0006980431],"category_scores_gemma":[0.0004154708,0.0001972918,0.0002861799,0.0003959227,0.0002080719,0.0003893191,0.0001851489,0.0002522461,0.0002560604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002559127,"about_ca_system_score_gemma":0.0002911485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002790473,"about_ca_topic_score_gemma":0.003843437,"domain_scores_codex":[0.9998904,0.00001414132,0.000005167215,0.00002433564,0.00004953707,0.00001640078],"domain_scores_gemma":[0.9998757,0.00002931753,0.00002553509,0.00001251139,0.00004406294,0.00001292985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005291575,0.0001396177,0.005018125,0.0002010481,0.00005548038,0.000275366,0.00008737041,0.01766825,0.4947005,0.0008773726,0.001225029,0.4792227],"study_design_scores_gemma":[0.00004685943,0.0003485103,0.02377217,0.00004657497,0.00007955194,0.0009207067,0.0001164472,0.6919864,0.278756,0.0007393918,0.003142902,0.00004450839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3932118,0.0008641427,0.6002552,0.00009700962,0.0000839401,0.0001341457,0.0001528626,0.001466721,0.003734198],"genre_scores_gemma":[0.7130115,0.0005606943,0.2847023,0.00004785038,0.0000265468,0.00003932125,0.0001723479,0.00003322081,0.001406285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002790473,"threshold_uncertainty_score":0.005548418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059584265696785,"score_gpt":0.2285963781090632,"score_spread":0.2180005354520954,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}